Hello and welcome! In today's lesson, we will be comparing Simple Moving Average (SMA) and Exponential Moving Average (EMA) using Tesla ($TSLA) stock data. This lesson will help you revise how to handle financial data with Pandas, calculate both SMA and EMA, and create visualizations to understand and compare these technical indicators.

Goal: By the end of this lesson, you will be able to load financial data, compute SMA and EMA, and visualize these indicators to make informed trading decisions.

Lesson Plan:

1. Loading and Preparing the Dataset
2. Calculating and Comparing SMA and EMA
3. Subsetting the Data for Visualization
4. Plotting SMA and EMA for Comparison

Let's start by loading the Tesla dataset and preparing it for analysis. We will convert the 'Date' column to datetime format and set it as the index.

import pandas as pd
from datasets import load_dataset

# Load the Tesla dataset
dataset = load_dataset('codesignal/tsla-historic-prices')

# Convert the dataset into a DataFrame
tesla_df = pd.DataFrame(dataset['train'])

# Convert the 'Date' column to datetime format
tesla_df['Date'] = pd.to_datetime(tesla_df['Date'])

# Set the 'Date' column as the index
tesla_df.set_index('Date', inplace=True)

We'll calculate the 20-day Simple Moving Average (SMA) and Exponential Moving Average (EMA) of the closing prices.

# Calculate the 20-day Simple Moving Average
tesla_df['SMA_20'] = tesla_df['Close'].rolling(window=20).mean()

# Calculate the 20-day Exponential Moving Average
tesla_df['EMA_20'] = tesla_df['Close'].ewm(span=20, adjust=False).mean()

To make the visualization clearer, we'll focus on the year 2018.

# Using a smaller date range for better visualization
tesla_df_small = tesla_df.loc['2018']

Finally, we will plot the closing prices along with the SMA and EMA to visualize and compare these indicators using Matplotlib.

import matplotlib.pyplot as plt

# Plotting
tesla_df_small[['Close', 'SMA_20', 'EMA_20']].plot(figsize=(12, 6), title="TSLA Close Price, SMA 20, and EMA 20 (2018)")
plt.show()

The output of the above code will be a visual line graph showing TSLA's closing price, along with the 20-day SMA and EMA values for the year 2018. This graphical representation allows us to visually compare the SMA and EMA against the actual closing prices, thus demonstrating their utility in trend analysis and trading decision-making.